# Rapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancers

> **NIH NIH R01** · SLOAN-KETTERING INST CAN RESEARCH · 2022 · $591,847

## Abstract

PROJECT SUMMARY
 Gynecologic cancers are some of the most lethal diseases affecting women. Globally, one woman dies of
cervical cancer every two minutes. MRI is increasingly used in the evaluation of gynecologic and many other
cancers. Beyond its established use for cancer staging, there has long been an interest in the use of MRI-derived
quantitative metrics to gain insights into the tumor microenvironment. Parametric maps obtained from
quantification of dynamic contrast enhanced (DCE) MRI data can be used to study tumor vascularity and identify
tumors that are better perfused and oxygenated and thus more sensitive to some treatments such as
chemotherapy and radiation. However, the relative slow imaging speed and motion sensitivity of current MRI
technology results in non-reliable and non-reproducible quantification of DCE-MRI data, which restricts its
application in clinical practice.
 Our group is a world leader in development of rapid motion-resistant DCE-MRI techniques, in particular using
combinations of radial imaging and compressed sensing. We developed the technique called GRASP, which
was conceived as an academic-industrial partnership and has now been successfully translated into standard
clinical practice. Though powerful, the first generation of GRASP has limitations. First, radial imaging is robust
to motion, but not free of motion, which usually results in blurring. Second, GRASP uses a very simple sparsifying
transform for compressed sensing, which can introduce issues with quantification. Third, GRASP was not
originally developed for pharmacokinetic analysis and misses important ingredients such as integration of AIF
estimation and T1 mapping. Fourth, image reconstruction time is still very long – in the order of several minutes.
 We have developed new advances to circumvent these limitations and offer a new DCE-MRI technique with
increased speed, motion-resistance and personalized AIF estimation and T1 mapping for pharmacokinetic
analysis. Following the PAR-18-009 guidelines, our main goal is to form an academic-industrial partnership
between Memorial Sloan Kettering Cancer Center and General Electric Healthcare to translate these new
developments in quantitative DCE-MRI for use in patients with gynecologic and other type of cancers. Specific
Aims are as follows:
1. Develop and implement a fast motion-resistant quantitative DCE-MRI technique that goes beyond GRASP
 to offer increased speed and resistance to motion; dynamic T1 mapping; and personalized and automated
 pharmacokinetic analysis
2. Evaluate the repeatability, reproducibility and preliminary tumor response assessment of the fast motion-
 robust quantitative DCE-MRI technique (“DCE-new”) and compare DCE-new to standard of care DCE-MRI
 (“DCE-standard”) in patients with gynecologic cancer
3. Develop and evaluate fast image reconstruction algorithms based on deep learning

## Key facts

- **NIH application ID:** 10432102
- **Project number:** 5R01CA244532-03
- **Recipient organization:** SLOAN-KETTERING INST CAN RESEARCH
- **Principal Investigator:** Ricardo Otazo
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $591,847
- **Award type:** 5
- **Project period:** 2020-09-22 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10432102

## Citation

> US National Institutes of Health, RePORTER application 10432102, Rapid motion-robust quantitative DCE-MRI for the assessment of gynecologic cancers (5R01CA244532-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10432102. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*